Tigers and their prey: Predicting carnivore densities from prey abundance

Abstract

The goal of ecology is to understand interactions that determine the distribution and abundance of organisms. In principle, ecologists should be able to identify a small number of limiting resources for a species of interest, estimate densities of these resources at different locations across the landscape, and then use these estimates to predict the density of the focal species at these locations. In practice, however, development of functional relationships between abundances of species and their resources has proven extremely difficult, and examples of such predictive ability are very rare. Ecological studies of prey requirements of tigers Panthera tigris led us to develop a simple mechanistic model for predicting tiger density as a function of prey density. We tested our model using data from a landscape-scale long-term (1995-2003) field study that estimated tiger and prey densities in 11 ecologically diverse sites across India. We used field techniques and analytical methods that specifically addressed sampling and detectability, two issues that frequently present problems in macroecological studies of animal populations. Estimated densities of ungulate prey ranged between 5.3 and 63.8 animals per km 2 . Estimated tiger densities (3.2-16.8 tigers per 100 km 2 ) were reasonably consistent with model predictions. The results provide evidence of a functional relationship between abundances of large carnivores and their prey under a wide range of ecological conditions. In addition to generating important insights into carnivore ecology and conservation, the study provides a potentially useful model for the rigorous conduct of macroecological science. E cological investigations basically involve efforts to understand interactions that determine the spatial distribution and abundance of organisms (1-3). Ecologists strive for a predictive science in which they can identify key attributes as potential limiting factors for a focal species, measure these attributes at different locations, and make predictions about the abundance of the focal species based on these measured attributes. An alternative popular approach to the study of spatial distribution and abundance is to search for patterns in existing data and then to treat perceived patterns as phenomenological models to be used for making predictions. Regardless of research approach, the study of the distribution and abundance of organisms at large spatial scales (i.e., macroecological patterns) has received substantial emphasis recently (3)(4)(5)(6)(7)(8).Analyses directed at macroecological questions require data collected at a scale far beyond the typical study areas of most field ecologists. As a result, such analyses are usually based on either large-scale count surveys of animal populations (6) or on metaanalyses of results from numerous individual studies (8). However, most large-scale count surveys of animal populations fail to yield strong inferences for two reasons: they are based on raw count data (indices) bearing an unknown relationship to true anima...

The shrinking ark: patterns of large mammal extinctions in India

“…We provide evidence that protected nature reserve areas are critical for reducing the local extinction probabilities of most Indian large mammals. India's current fragmented network of relatively small protected areas (average size less than 300 km 2 ) does have high carrying capacities for large mammals (Karanth et al 2004). However, given the overall patterns of species extinction estimated in this study, creation of new protected areas and interconnection of existing protected areas will be required through conservation policy and management if many of these mammals are to persist into the future.…”

Estimating densities of large herbivores in tropical forests: Rigorous evaluation of a dung‐based method

“…Distance sampling estimates of density can be biased, either upward or downward, when assumptions of line‐transect sampling are not fully satisfied (Buckland et al., 2001; Thomas & Karanth, 2002). Although we followed field and analytic protocols correctly (Karanth et al., 2002), it is plausible that the fundamental assumptions of line‐transect sampling—such as animals directly on the line are always detected and that animals are detected at their initial location prior to any movement in response to the observer—were violated and biased the density estimates. On the other hand, though we were confident that we never missed a dung pile within a plot during our counts, it is still possible that our dung counts were inflated.…”

“…Data gathered from (b) and (c) were then used to compute the perpendicular distance from the animal cluster to the transect line. For further details of the distance sampling field protocols used, please refer to Karanth, Thomas, and Kumar (2002). …”

“…We included each habitat type as a stratum in DISTANCE and obtained stratum‐specific estimates of all distance sampling‐related parameters for each species (see Supporting Information Table S4). For further details of the analyses, please refer to Thomas and Karanth (2002). …”

Reintroduction of Top‐Order Predators: Using Science to Restore One of the Drivers of Biodiversity

“…Even today, those top-order predators that persist continue to be persecuted by humans-be they brown bears, Ursus arctos, in Europe (Gentleman, 2004;Hunter, 2006); snow leopards, Uncia uncia, in the Himalayas (Bagchi & Mishra, 2006); or jaguars, Panthera onca, in Venezuela (Polisar et al, 2003). Direct persecution may not be the sole threat, as a decline in prey will invariably lead to a decline in predator abundance through bottom-up limitation (Karanth et al, 2004;Hayward et al, 2007d).…”